面向道路异常检测的自适应低光增强：多架构基准与边缘部署研究
Adaptive Low-Light Enhancement for Road Anomaly Detection: A Multi-Architecture Benchmark and Edge Deployment Study
Obed Honour Eje, Oluwatosin Kolade, Glory Bagai, Taliha Abiodun Folorunso
AI 速读 基于摘要
论文在物理光照与天气退化条件下基准测试四种目标检测架构，发现统一使用SCI低光增强在黄昏光照下反而有害，并提出一种免训练的亮度自适应路由方法以恢复大部分性能损失，并在Jetson Nano上验证。
cs.CV
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Road anomaly detection systems on autonomous vehicles must function reliably under degraded visual conditions including darkness, rain, and Harmattan dust haze. This paper benchmarks four object detection architectures, YOLOv8n, YOLOv12n, RT-DETR, and Faster R-CNN, under physically grounded lighting and weather degradation, and shows that uniform application of the SCI low-light enhancement network is actively harmful at dusk-level lighting (-16.1% to -30.7% mAP@50). We propose a lightweight, training-free luminance-adaptive router that selects between no enhancement, moderate SCI, and denoising plus strong SCI based on per-frame Rec. 601 luminance, recovering most of the dusk penalty while preserving low-light gains, and we validate the full pipeline on NVIDIA Jetson Nano edge hardware.